[BOOK][B] Sentiment analysis and opinion mining
B Liu - 2022 - books.google.com
Sentiment analysis and opinion mining is the field of study that analyzes people's opinions,
sentiments, evaluations, attitudes, and emotions from written language. It is one of the most …
sentiments, evaluations, attitudes, and emotions from written language. It is one of the most …
A survey of opinion mining and sentiment analysis
Sentiment analysis or opinion mining is the computational study of people's opinions,
appraisals, attitudes, and emotions toward entities, individuals, issues, events, topics and …
appraisals, attitudes, and emotions toward entities, individuals, issues, events, topics and …
[BOOK][B] Sentiment analysis: Mining opinions, sentiments, and emotions
B Liu - 2020 - books.google.com
Sentiment analysis is the computational study of people's opinions, sentiments, emotions,
moods, and attitudes. This fascinating problem offers numerous research challenges, but …
moods, and attitudes. This fascinating problem offers numerous research challenges, but …
Aspect and entity extraction for opinion mining
Opinion mining or sentiment analysis is the computational study of people's opinions,
appraisals, attitudes, and emotions toward entities such as products, services, organizations …
appraisals, attitudes, and emotions toward entities such as products, services, organizations …
Opinion mining and sentiment analysis
B Liu, B Liu - Web data mining: exploring hyperlinks, contents, and …, 2011 - Springer
In Chap. 9, we studied the extraction of structured data from Web pages. The Web also
contains a huge amount of information in unstructured texts. Analyzing these texts is of great …
contains a huge amount of information in unstructured texts. Analyzing these texts is of great …
[PDF][PDF] Instance selection and instance weighting for cross-domain sentiment classification via PU learning
Due to the explosive growth of the Internet online reviews, we can easily collect a large
amount of labeled reviews from different domains. But only some of them are beneficial for …
amount of labeled reviews from different domains. But only some of them are beneficial for …
[PDF][PDF] Negative training data can be harmful to text classification
This paper studies the effects of training data on binary text classification and postulates that
negative training data is not needed and may even be harmful for the task. Traditional binary …
negative training data is not needed and may even be harmful for the task. Traditional binary …
Automated Machine Learning for Positive-Unlabelled Learning
JD Saunders - 2023 - search.proquest.com
Positive-Unlabelled (PU) learning is a field of machine learning that involves learning
classifiers from data consisting of positive class and unlabelled instances. That is, instances …
classifiers from data consisting of positive class and unlabelled instances. That is, instances …
Entity set expansion in knowledge graph: a heterogeneous information network perspective
Entity set expansion (ESE) aims to expand an entity seed set to obtain more entities which
have common properties. ESE is important for many applications such as dictionary …
have common properties. ESE is important for many applications such as dictionary …
Course concept expansion in moocs with external knowledge and interactive game
As Massive Open Online Courses (MOOCs) become increasingly popular, it is promising to
automatically provide extracurricular knowledge for MOOC users. Suffering from semantic …
automatically provide extracurricular knowledge for MOOC users. Suffering from semantic …